Microstructure and Mechanical Property Evaluation of Dune Sand Reactive Powder Concrete Subjected to Hot Air Curing

Compressive strength prediction of admixed HPC concrete by hybrid deep learning approaches

The estimation of compressive strength includes time-consuming, finance-wasting, and laboring approaches to undertaking High-performance concrete (HPC) production. On the other side, a vast volume of concrete consumption in industrial construction requires an optimal mix design with different percentages to reach the highest compressive strength. The present study considered two deep learning approaches to handle compressive strength prediction. The robustness of the deep model was put high through two novel optimization algorithms as a novelty in the research world that played their precise roles in charge of model structure optimization. Also, a dataset containing cement, silica fume, fly ash, the total aggregate amount, the coarse aggregate amount, superplasticizer, water, curing time, and high-performance concrete compressive strength was used to develop models. The results indicate that the AMLP-I and GMLP-I models served the highest prediction accuracy. R2 and RMSE of AMLP-I stood at 0.9895 and 1.7341, respectively, which declared that the AMLP-I model could be presented as the robust model for estimating compressive strength. Generally, using optimization algorithms to boost the capabilities of prediction models by tuning the internal characteristics has increased the reliability of artificial intelligent approaches to substitute the more experimental practices.

Evaluation of Workability and Structuration Rate of Locally Developed 3D Printing Concrete Using Conventional Methods

Concrete 3D printing is a novel construction method that can bring new horizons to the construction industry. However, there are still many challenges that limit its capabilities. Despite the huge research efforts, to date, there are still no standardized acceptance criteria and guidelines for the evaluation of printing concrete. Therefore, the main objective of this research was to develop 3D printing mixes with different aggregate-to-binder (a/b) ratios (1.2, 1.5, and 1.8) and evaluate it in terms of its fresh printing properties, which include the workability, extrudability, setting time, open time, and buildability. The compressive strengths of cast and printed specimens were also tested to determine the effect of the layering process. The workability was evaluated using commonly used devices in the construction industry (slump and flow table test) and was monitored over time along with the penetration test to indicate the structuration rate of concrete. From the experimental results and observations, the flow test resulted in the best indication of the structuration rate (thixotropy) of concrete, followed by the penetration and slump tests. The a/b ratio affected all the investigated properties of the printing concrete. Higher a/b ratios resulted in increased structuration rate, buildability, and compressive strength of cast specimens. However, for printed specimens, the compressive strength decreased with the increase in a/b ratio due to increased thixotropy. Therefore, from the results of the present investigation, it can be concluded that high a/b ratios (>1.5) are not desirable for printing concrete.